August 19, 2016 @ 11:00 am – 12:00 pm
Rooms 407A/B BAUM
5607 Baum Blvd.
The Offices at Baum


Ioannis Tsamardinos, PhD, Associate Professor, Department of Computer Science, University of Crete, Greece
Logic-Based Causal Discovery for Heterogeneous Datasets

Abstract: Computational Causal Discovery aims to induce causal models, causal networks, and causal relations from observational data without performing or by performing only few interventions (perbutations, manipulations) of a system. A relatively recent approach to causal discovery, which we call logic-based integrative causal discovery, will be presented. This approach accepts and reasons with multiple heterogeneous datasets that are obtained under different sampling criteria, different experimental conditions (perbubations, interventions), and measuring different quantities (variables).  Applications to business data will be discussed. In addition, current complementary research directions in our lab will be presented, including scaling up to Big Data, learning of mechanistic causal models, the MXM R package capabilities and others.

Biography:  Dr. Tsamardinos acquired his Ph.D. 2001 in the Intelligent Systems Program from Pittsburgh University. He is Associate Professor at the Computer Science Department of University of Crete and co-founder of Gnosis Data Analysis IKE. His interest include the fields of machine learning, bioinformatics, causal discovery, and feature selection.

Dr. Tsamardinos has over 90 publications in international journals, conferences, and books. He has participated in several national, EU, and US funded research projects. Statistics on recognition of work include more than 4800 citations, and h-index of 28. Dr. Tsamardinos has recently been awarded the European and Greek national grants of excellence, the ERC Consolidator and the ARISTEIA II grants respectively (the equivalent of the NSF Young Investigator Award).